AIMC Topic: Emergency Service, Hospital
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System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Sep 4, 2020
PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited...
A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses.
Journal of orthopaedic surgery and research
Aug 14, 2020
OBJECTIVE: Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classif...
Applying Artificial Intelligence to ECG Analysis: Promise of a Better Future.
Circulation. Arrhythmia and electrophysiology
Aug 4, 2020
Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.
Annals of emergency medicine
Jul 24, 2020
STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current dec...
Predictors of emergency department opioid administration and prescribing: A machine learning approach.
The American journal of emergency medicine
Jul 18, 2020
INTRODUCTION: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emergency Department (ED) setting, where providers are now confronted with balancing pain management and p...
Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.
Emergency radiology
Jul 8, 2020
PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment ...
The first use of artificial intelligence (AI) in the ER: triage not diagnosis.
Emergency radiology
Jul 8, 2020
Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at all. The use of AI appears to show significant promise in ER triage in the present. We briefly disc...
Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.
BMJ open
Jul 5, 2020
OBJECTIVES: We investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for timely hospital visits.
Using Artificial Intelligence to Predict Surgical Shunts in Men with Ischemic Priapism.
The Journal of urology
Jun 9, 2020
PURPOSE: Ischemic priapism is a urological emergency that requires prompt intervention to preserve erectile function. Characteristics that influence escalation to surgical intervention remain unclear. We identified factors and developed machine learn...
Male
Middle Aged
Risk Assessment
Adolescent
Priapism
Penis
Aged
Urologic Surgical Procedures, Male
Aged, 80 and over
Young Adult
Likelihood Functions
Adult
Penile Erection
Humans
Emergency Treatment
Anastomosis, Surgical
Machine Learning
Phenylephrine
Retrospective Studies
Paracentesis
Erectile Dysfunction
Time Factors
Treatment Outcome
Models, Statistical
Emergency Service, Hospital
Predicting hospital admission for older emergency department patients: Insights from machine learning.
International journal of medical informatics
May 16, 2020
BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and com...
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